Offline signature verification by combining graph edit distance and triplet networks
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Authors
Maergner, Paul
Pondenkandath, Vinaychandran
Alberti, Michele
Liwicki, Marcus
Ingold, Rolf
Author (Corporation)
Publication date
2018
Typ of student thesis
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Type
04B - Conference paper
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Parent work
Structural, syntactic, and statistical pattern recognition. Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, August 17-19, 2018. Proceedings
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DOI of the original publication
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Series
Lecture notes in computer science
Series number
11004
Volume
Issue / Number
Pages / Duration
470-480
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Publisher / Publishing institution
Springer
Place of publication / Event location
Cham
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Abstract
Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties.
Keywords
Subject (DDC)
330 - Wirtschaft
Event
S+SSPR 2018. IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR 2018) and Structural and Syntactic Pattern Recognition (SSPR2018)
Exhibition start date
Exhibition end date
Conference start date
17.08.2018
Conference end date
19.08.2018
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ISBN
978-3-319-97784-3
978-3-319-97785-0
978-3-319-97785-0
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Language
English
Created during FHNW affiliation
Yes
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Publication status
Published
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Closed
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Citation
MAERGNER, Paul, Vinaychandran PONDENKANDATH, Michele ALBERTI, Marcus LIWICKI, Kaspar RIESEN, Rolf INGOLD und Andreas FISCHER, 2018. Offline signature verification by combining graph edit distance and triplet networks. In: Xiao BAI, Edwin R. HANCOCK, Tin Kam HO, Richard C. WILSON, Battista BIGGIO und Antonio ROBLES-KELLY (Hrsg.), Structural, syntactic, and statistical pattern recognition. Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, August 17-19, 2018. Proceedings. Cham: Springer. 2018. S. 470–480. Lecture notes in computer science, 11004. ISBN 978-3-319-97784-3. Verfügbar unter: https://irf.fhnw.ch/handle/11654/42436